Nicole Hamler¶

"On my honor, as a student, I have neither given nor received unauthorized aid on this academic work."¶

Geospatial Analysis using GeoPandas - Part 3¶

%matplotlib inline

from __future__ import (absolute_import, division, print_function)
import os

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np

import seaborn as sns
plt.style.use('bmh')

import pandas as pd
import geopandas as gpd
from geopandas import GeoSeries, GeoDataFrame
from geopandas.tools import sjoin

from shapely.geometry import Point, LineString, Polygon

# ROAD MAP DATASET  (naturalearthdata.com)
rd = gpd.read_file("data\\ne_10m_roads.shp")

(cont. from Part 2)¶

13. Interactive maps - Bokeh¶

# Importing Bokeh libraries 
from bokeh.plotting import figure, save
from bokeh.io import output_notebook, show
output_notebook()

from bokeh.models import GeoJSONDataSource
from bokeh.plotting import figure, output_file, show

# Initialize plot (p) and assign title
p = figure(title="Hello World")
p

a) Using ColumnDataSource as geo source¶

# Importing ColumnDataSource to allow Bokeh to read and store the data
from bokeh.models import ColumnDataSource

# Unlike Shapely, Bokeh can't read files which have geometry objects 
inter = gpd.read_file("data\\ne_10m_populated_places_simple.shp")
inter.head(2)

# Getting separate x and y values from the geometry objects contained in the file via the function getPointCoords
def getPointCoords(row, geom, coord_type):
    if coord_type =='x':
        return row[geom].x
    elif coord_type =='y':
        return row[geom].y

# Calculating coordinates for each column:
inter['x'] = inter.apply(getPointCoords,geom='geometry', coord_type='x', axis=1)
inter['y'] = inter.apply(getPointCoords,geom='geometry', coord_type='y', axis=1)

# Drop the geometry column to allow for Bokeh to read the file correctly
inter2 = inter.drop('geometry', axis=1).copy()
inter2.head(2)

# Creating the ColumnDataSource object
psource = ColumnDataSource(inter2)
psource

# Initializing plot figure
p = figure(title="Cities of the World!")

# Adding points to the plot via our ColumnDataSource object
p.circle('x', 'y', source=psource, color='blue', size=1)

# File path 
outfp = r"data/point_map2.html"

# Save the map
save(p, outfp)

'C:\\Users\\nhamler\\Desktop\\665\\Tutorial_GeoPandas\\data\\point_map2.html'

show (p)

b) GeoJSONDataSource¶

Seamless transition to newer GeoJsONDataSource functionality instead of ColumnDataSource

# Importing Sample Dataset included in Bokeh
from bokeh.sampledata.sample_geojson import geojson

# Mapping of sample data using GeoJSONDataSource
geo_source = GeoJSONDataSource(geojson=geojson)

# Initializing figure
p = figure()
p.circle(x='x', y='y', alpha=0.9, source=geo_source)

# Assigning file path
output_file("geojson.html")
show(p)

c) Bokeh and Google Maps¶

Bokeh's GMapPlot supports Googlemaps and enables Bokeh plots to be layered over Google Maps

In order to enable this function, a personal API key must be obtained from Google:
- https://developers.google.com/maps/documentation/javascript/get-api-key

Creating custom points to add to Google Map¶

# Import Bokeh functions
from bokeh.io import output_file, show
from bokeh.models import (GMapPlot, GMapOptions, ColumnDataSource, Circle, DataRange1d, PanTool, WheelZoomTool, BoxSelectTool)

# Display options of the Google Map
map_options = GMapOptions(lat=33.74, lng=-84.3880, map_type="roadmap", zoom=11)

# Assigning plot
plot = GMapPlot(
    x_range=DataRange1d(), y_range=DataRange1d(), map_options=map_options)

# Assigning title
plot.title.text = "Atlanta"

# Enter API key obtained from Google
plot.api_key = "AIzaSyASeDwXJrTLdxsgtTfdpUAfMxdtl8mZHZw"

# Creating points to be added to the map
source = ColumnDataSource(
    data=dict(
        lat=[33.74316,33.74319, 33.74314],
        lon=[-84.36, -84.32, -84.39],))

# Initializing Point display options
circle = Circle(x="lon", y="lat", size=15, fill_color="blue", fill_alpha=0.8, line_color=None)

# Adding points and display options to the plot
plot.add_glyph(source, circle)

# Add customization tool options for plot display
plot.add_tools(PanTool(), WheelZoomTool(), BoxSelectTool())
# Create file path
output_file("gmap_plot.html")
show(plot)

Adding crime dataset to Google Map:¶

crime = pd.read_excel("data\COBRADATA2016.xlsx")
crime.head(2)

# Creating dictionary to hold the values for coordinates and point details
crime_source = ColumnDataSource(data=dict(x=crime['x'],
                                      y=crime['y'],
                                      neighborhood=crime['neighborhood'].values,
                                      crimes=crime['crimes'].values,
                                      victims=crime['MaxOfnum_victims'].values))

# Initializing the map options for the google map
map_options = GMapOptions(lat=33.74, lng=-84.3880, map_type="roadmap", zoom=11)

# Creating the GMapPlot based on the Google Map
plot = GMapPlot(
    x_range=DataRange1d(), y_range=DataRange1d(), map_options=map_options)

# Assinging title
plot.title.text = "Atlanta"

# Entering API key to enable Google Map functionality
plot.api_key = "AIzaSyASeDwXJrTLdxsgtTfdpUAfMxdtl8mZHZw"

# Display options for points
circle = Circle(x="x", y="y", size=2.5, fill_color="black", fill_alpha=0.8, line_color=None)

# Adding crime data and display options to the plot
plot.add_glyph(crime_source, circle)

plot.add_tools(PanTool(), WheelZoomTool(), BoxSelectTool())
output_file("gmap_plot.html")

show(plot)

d) Hover Tool¶

Adding the hover tool to display neighborhood, type of crime, and number of victims for each of the points.¶

# Importing the HoverTool
from bokeh.models import HoverTool

# Initializing the HoverTool and the information to be shown for each point
my_hover = HoverTool()

my_hover.tooltips = [("Crime:","@crimes"), ("Neighborhood:","@neighborhood"), ("No. of victims:", "@victims")]

# Adding the hover tool to the existing map
plot.add_tools(my_hover)

show(plot)

	scalerank	natscale	labelrank	featurecla	name	namepar	namealt	diffascii	nameascii	adm0cap	...	pop_other	rank_max	rank_min	geonameid	meganame	ls_name	ls_match	checkme	min_zoom	geometry
0	10	1	8	Admin-1 capital	Colonia del Sacramento			0	Colonia del Sacramento	0.0	...	0	7	7	3443013.0			0	0	9.0	POINT (-57.84000247340134 -34.47999900541754)
1	10	1	8	Admin-1 capital	Trinidad			0	Trinidad	0.0	...	0	7	7	3439749.0			0	0	9.0	POINT (-56.90099656015872 -33.5439989373607)

	scalerank	natscale	labelrank	featurecla	name	namepar	namealt	diffascii	nameascii	adm0cap	...	rank_max	rank_min	geonameid	meganame	ls_name	ls_match	checkme	min_zoom	x	y
0	10	1	8	Admin-1 capital	Colonia del Sacramento			0	Colonia del Sacramento	0.0	...	7	7	3443013.0			0	0	9.0	-57.840002	-34.479999
1	10	1	8	Admin-1 capital	Trinidad			0	Trinidad	0.0	...	7	7	3439749.0			0	0	9.0	-56.900997	-33.543999

	MI_PRINX	offense_id	rpt_date	occur_date	occur_time	poss_date	poss_time	beat	apt_office_prefix	apt_office_num	...	dispo_code	MaxOfnum_victims	Shift	Avg Day	loc_type	crimes	neighborhood	npu	x	y
0	5637549	150102493	01/10/2016	01/10/2016	22:00:00	01/10/2016	22:00:00	511	NaN	NaN	...	NaN	1.0	Eve	Sun	21.0	BURGLARY-NONRES	Downtown	M	-84.39487	33.75757
1	5641270	150611492	03/01/2016	02/25/2016	12:00:00	02/29/2016	19:00:00	412	NaN	NaN	...	NaN	1.0	Unk	Unk	20.0	LARCENY-NON VEHICLE	Vine City	L	-84.41411	33.75823